基于最小流形类内离散度的支持向量机.

Support vector machine (SVM) is one of the most popular classification methods and widely used in practice. But with the development of application, it encounters a problem which seriously limits the classification efficiency: it only focuses on the margin between classes, but ignores the class distributions. In order to solve the above problem, this paper proposed minimum class variance support vector machine (MCVSVM) by Zafeiriou and considered boundary information and distribution characteristics and therefore its classification efficiency was much better than SVM. The local characteristics of each class was quite important but it was regrettable that it was neglected by both SVM and MCVSVM. In view of this, this paper proposed support vector machine based on minimum manifold-based within-class scatter ( SVM-M WCS). The theoretical and experimental analysis shows the effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]